How to Read and Interpret FTIR Spectra for Materials: A Master Dataset with Step-by-Step Guided Peak-Correlation Analysis, Representative Examples, and a Foundation for Future Artificial Intelligence (AI)-Assisted Analysis

Abstract

Fourier Transform Infrared (FTIR) spectroscopy is widely used for materials characterization; however, spectrum interpretation often relies on isolated peak identification, which can lead to ambiguity, especially for complex materials. This study presents a master FTIR dataset combined with a step-by-step guided peak-correlation workflow to support systematic and reproducible FTIR interpretation. The dataset organizes FTIR information into five spectral regions and emphasizes correlated peak families rather than individual bands. Representative examples covering simple compounds, organic compounds, polymers, and halogenated materials demonstrate the applicability of the approach across materials science and chemical engineering fields. Beyond manual interpretation, the structured dataset and workflow are designed to be machine-readable and extensible, enabling future integration with data-driven methods such as artificial intelligence (AI) and machine learning (ML) for automated spectral analysis. This work provides a practical reference for FTIR interpretation, education, and the development of intelligent materials characterization systems.